




'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def __UpperCAmelCase	(   _UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Any						)      ->      Any:
			# Initialise PyTorch model
			__snake_case			=		TaConfig.from_json_file(_UpperCAmelCase						)
			print(F'''Building PyTorch model from configuration: {config}'''						)
			__snake_case			=		TaForConditionalGeneration(_UpperCAmelCase						)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase						)

			# Save pytorch-model
			print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
			model.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
						a       :						List[str]								=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						a       :						Any								=		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

